Social inequalities in access to healthcare among the population aged 50+ years during the COVID-19 pandemic in Europe
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Social inequalities in access to healthcare
among the population aged 50+ years
during the COVID-19 pandemic in Europe
Louis Arnault, Florence Jusot, Thomas Renaud
Working Paper Series 58-2021
DOI: 10.17617/2.3289765
This project has received funding from the European
Union under grant agreements VS/2019/0332,
VS/2020/0313 and the European Union’s Horizon 2020
research and innovation programme under grant
agreements No 870628, No 101015924.Acknowledgements Research in this article is a part of the H2020 SHARE-COVID19 project (Grant agreement No. 101015924). This publication is based on preliminary SHARE wave 8 release 0 data (Börsch-Supan 2020). Therefore, the analyses, conclusions and results are preliminary. Please see Scherpenzeel et al. (2020) for methodological details. In addition, this paper uses data from SHARE Waves 1, 2, 3, 4, 5, 6 and 7 (DOIs: 10.6103/SHARE.w1.710, 10.6103/SHARE.w2.710, 10.6103/SHARE.w3.710, 10.6103/SHARE.w4.710, 10.6103/SHARE.w5.710, 10.6103/SHARE.w6.710, 10.6103/SHARE.w7.710), see Börsch-Supan et al. (2013) for methodological details. The SHARE data collection has been funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE- PREP: GA N°211909, SHARE-LEAP: GA N°227822, SHARE M4: GA N°261982, DASISH: GA N°283646) and Horizon 2020 (SHARE-DEV3: GA N°676536, SHARE-COHESION: GA N°870628SERISS: GA N°654221, SSHOC: GA N°823782) and by DG Employment, Social Affairs & Inclusion. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1- AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged, (see www.share-project.org).
Social inequalities in access to healthcare among the population aged
50+ years during the COVID-19 pandemic in Europe
Louis ARNAULT*, Florence JUSOT*°, Thomas RENAUD*
* Université Paris Dauphine, PSL, Paris, France
° IRDES (Institut de Recherche et Documentation en Economie de la Santé), Paris, France
Corresponding author: Thomas RENAUD (thomas.renaud@dauphine.psl.eu), ORCID: 0000-0002-4895-6090
Abstract
This study investigated social inequalities in access to healthcare during the first wave of the
coronavirus disease 2019 (COVID-19) epidemic in Europe among adults aged 50 years and older, using
data from the regular administration of the Survey of Health, Ageing and Retirement in Europe (SHARE)
and the specific telephone survey administered regarding COVID-19 (SHARE Corona Survey). It
addressed three main research questions: did people who were in difficult economic situations before
the epidemic face more barriers to access healthcare than others? If so, to what extent can these
discrepancies be attributed to initial differences in health status and the use of care between social
groups or to differential effects of the pandemic on these groups? Did social inequalities with regard
to unmet needs during the pandemic differ across countries? Unmet healthcare needs are
characterised by three types of behaviours likely to be induced by the pandemic: forgoing care for fear
of contracting COVID-19, having pre-scheduled care postponed, and being unable to obtain medical
appointments or treatments when needed.
Our results substantiate the existence of social inequalities in accessing healthcare during the
pandemic and of cumulative effects of economic and medical vulnerabilities: the impact of economic
vulnerability is notably stronger among those who were in poor health before the outbreak and thus
are potentially the oldest individuals. The cross-country comparison highlighted heterogeneous effects
of economic vulnerability on forgoing care and having care postponed among countries, which are not
comparable to the initial cross-country differences in social inequalities in access to healthcare.
Keywords: COVID-19; social inequalities; healthcare; unmet needs1. Introduction
The management of the coronavirus 2019 (COVID-19) pandemic has compelled countries to undertake
major reorganisations of their healthcare systems, which led to drastic healthcare rationing. The
activities of some health professionals – specialist doctors, dentists and physiotherapists – have been
put on hold, and programmed care has been rescheduled (WHO, 2020; Søreide et al., 2020). In the
meantime, the demand for care may also have changed. Shortages in the supply of healthcare and
travel restrictions may have increased transport and transaction costs incurred by accessing
healthcare. Some people may also have forgone care for fear of contracting COVID-19; this fear is
potentially stronger in the population at risk for severe COVID-19, such as elderly and chronically ill
individuals, and is exacerbated by difficulties in obtaining and processing relevant health information.
A substantial decrease in the amount of emergency and scheduled care provided has been observed
in most countries. In the US, Chatterji and Li (2020) reported a 67% reduction in outpatient visits during
the third week of April 2020 compared to a baseline pre-epidemic week and a 25% reduction by mid-
May. A dramatic decrease in the provision of healthcare has also been observed with regard to primary
care visits, visits to emergency departments, and elective surgeries (Hartnett et al., 2020; Mehrotra et
al., 2020), which seems to be causally linked to ‘stay-at-home’ and nonessential business closure
policies (Ziedan et al., 2020). In France, ambulatory care expenditures decreased by 12% during the
lockdown compared to the same period the year before (CNAMTS, 2020). England has seen a sharp
decrease of 57% in visits to emergency departments in April-May compared to the year before,
followed by a gradual upturn thereafter (NHS, 2020). In Belgium, 75% of the population reported
having forgone planned healthcare since the start of the outbreak (Bertier et al., 2020). On average,
European countries have suffered a partial or complete disruption in the provision of 40% of essential
health services in hospitals, primarily preventive, emergency and oncological care (WHO, 2020).
Elective surgery was notably affected: more than 2 million surgical procedures per week were
cancelled or postponed worldwide during the peak weeks of disruption (Negopdiev et al., 2020).
It is therefore important to identify which populations have suffered most from these disruptions in
healthcare provision, as these are likely to have major long-term effects on the health of these
populations. Preliminary studies have established that delays in diagnosis and treatment in the UK
could lead to a 5% to 15% increase in the number of deaths from cancer up to 5 years after diagnosis
(Maringe et al., 2020). Given the magnitude of pre-existing social inequalities in health and in
healthcare utilisation, the impact of the epidemic on these inequalities needs to be addressed.
Reducing inequalities in health and access to healthcare between social groups is one of the major
public health objectives in Europe (Marmot et al., 2008; Marmot and Bell, 2016). It is therefore crucial
to determine to what extent the COVID-19 pandemic stands as a potentially serious obstacle to
achieving this goal.
Evidence of social inequalities in healthcare use – i.e., disparities in the utilisation of healthcare for the
same health need depending on the socio-economic level – has been extensively described in
European countries, particularly regarding visits to specialists and dentists, preventive care, and the
risk of forgoing care due to costs, distance or waiting time (OECD, 2019), especially among older adults
(Jürges and Stella, 2019; Litwin and Sapir, 2009). The channels through which pandemics and situations
involving care rationing can shape these healthcare inequalities are multifaceted. First, the most
socially vulnerable people and those most “disconnected” from the healthcare system may have been
isolated even more than usual. The pandemic may also have modified people’s demand for healthcare
in distinctive manners depending on their social status. We can assume that more educated people
were able to better adjust their demand for healthcare to their actual level of need, while less educated
2people might have suffered more from barriers to information and care restrictions. Fear of
contracting COVID-19 may also differ socially due to differences in exposure to the virus at home or at
work, differences in the likelihood of developing severe COVID-19 related to differences in the
prevalence of chronic diseases, and heterogeneity in risk perception and risk aversion. Conversely,
health systems may have adopted a general policy of healthcare resource prioritisation in favour of
people with the highest need, especially older people with comorbidities, irrespective of any other
characteristic (Hanna et al., 2020; Pikoulis et al., 2020; Rosenbaum, 2020). The rationing of planned
and specialist care may also have had a greater impact on those who usually make more use of these
types of care, especially the more socio-economically advantaged. Given the differences in the
organisation of healthcare systems, responses to the pandemic and initial levels of social inequalities
in healthcare utilisation across countries, it seems also appropriate to assess the between-country
variations in the consequences of the epidemic with regard to social inequalities in healthcare use.
To date, only a few studies have analysed social disparities in the likelihood of unmet care needs
specific to the COVID-19 pandemic, mainly for the US. Gonzalez et al. (2020) found a clear effect of
social deprivation on unmet needs: the probability of avoiding getting care due to concerns about
exposure to the virus affected 36% of the people who lost work or work-related income during the
pandemic and only 25% of the others. Another study revealed that delaying or avoiding obtaining
urgent medical care due to concerns about COVID-19 was more common among the less well-educated
and usually underprivileged ethnic groups, although no difference was observed after stratification by
income level (Czeisler et al., 2020). Racial and ethnic inequities in access to dental care during the
pandemic have also been observed (Kranz et al., 2020).
This study investigated social inequalities in unmet needs —forgoing care due to the fear of contracting
COVID-19, postponing scheduled care and finding it impossible to obtain a medical appointment or
treatment— during the first wave of the COVID-19 epidemic in 26 European countries participating in
SHARE. It addresses the following questions: Did people in difficult economic situations before the
epidemic face more barriers to access healthcare than others? If so, to what extent can these
discrepancies be attributed to initial differences in health status and use of care between social groups
or to differential effects of the pandemic on these groups? Did social inequalities in unmet needs
during the pandemic differ across countries?
2. Data and methods
2.1. Sample selection
This research is based on Survey of Health, Ageing and Retirement in Europe (SHARE) panel data
(Börsch-Supan et al., 2013) derived from both the eighth wave of regular data collection and the SHARE
Corona Survey, the ad hoc telephone survey focused on the impact of the COVID-19 crisis conducted
among SHARE panel households in June-July 2020 (Scherpenzeel et al., 2020).
The sample was restricted to participants aged 50 years and over living in private households who
participated in both the regular face-to-face wave 8 and SHARE Corona Survey and for whom there no
missing values for any of the variables involved in the analyses. In the end, the sample included 31,928
respondents.
32.2. Measuring unmet healthcare needs during the first wave of the epidemic
Inequalities in access to care are here identified by the subjective assessment of unmet needs due to
barriers to healthcare, which have been shown to be associated with the subsequent deterioration of
health status and thus to provide policy-relevant information for measuring inequity (Allin et al., 2010;
Dourgnon et al.; 2012; Ko, 2016). Subjective unmet needs can be defined as the gap between the
amount of healthcare received by an individual and the amount of healthcare he/she desires based on
his/her needs and preferences with regard to health and healthcare. Consequently, forgoing care may
be more common among those with a higher demand for health services for a given healthcare need,
particularly among the more affluent.
Dimensions of unmet healthcare needs are captured by the SHARE Corona Survey through the
assessment of three different kinds of barriers to accessing health services that have emerged since
the start of the epidemic for various reasons: 1) forgoing healthcare due to the fear of contracting
COVID-19 (fear): “Since the outbreak of Corona, did you forgo medical treatment because you were
afraid to become infected by the corona virus?”; 2) having planned healthcare cancelled or rescheduled
(postponement): “Did you have a medical appointment scheduled, which the doctor or medical facility
decided to postpone due to Corona?”; and 3) being unable to obtain a medical appointment
(unavailability): “Did you ask for an appointment for a medical treatment since the outbreak of Corona
and did not get one?”.
It is worth analysing these three indicators separately insofar as they presumably reflect different
reasons for not accessing healthcare and different types of unmet needs. The decision to forgo care
for fear of contracting COVID-19 can a priori apply to any type of care. However, postponement implies
that accessing healthcare was originally scheduled – an appointment with a specialist, a planned
medical exam, an elective surgery, etc. – and therefore might be more common in people with poor
health and chronic conditions. Impossibility of obtaining a medical appointment are instead associated
with a request for more immediate medical care, which is usually provided by first-line health
professionals in response to acute health problems.
2.3. Measuring economic vulnerability prior to the outbreak
Economic vulnerability before the outbreak is assessed based on the self-reported difficulty of “making
ends meet” in SHARE wave 8, which is collected in the following question: “Thinking of your
household’s total monthly income, would you say that your household is able to make ends meet…?
With great difficulty/with some difficulty/fairly easily/easily”. Respondents who express some or great
difficulty are said to be economically vulnerable.
This question originates from pioneering works on the poverty line (Goedhart et al., 1977) and has the
advantage of providing a synthetic and subjective measure of living conditions, comparable in principle
between countries regardless of cultural norms (Fahey, 2007).
2.4. Measuring levels of healthcare needs and utilisation prior to the outbreak
The most disadvantaged socio-economic groups are generally in worse health and tend to have lower
levels of healthcare utilisation for identical health needs in most countries (OECD, 2019). Investigating
the social inequalities in unmet needs during the pandemic thus requires disentangling the influence
of economic vulnerability per se from the confounding effects due to initial differences in baseline
health status and healthcare utilisation between social groups.
Two different sets of measures were then used.
4First, we sought to characterise individuals with serious chronic conditions, differentiating between
diseases identified in the medical literature as putting people at higher risk for developing severe
COVID-19 (Williamson et al., 2020; Zheng et al., 2020; Zhou et al., 2020), who may have been more
likely to have forgone care for fear of contracting COVID-19, and other chronic diseases. In SHARE, lists
of chronic conditions and drug treatments taken at least once a week are provided to respondents.
Respondents were defined as having a chronic condition that increases the risk of severe COVID-19
when they reported that they had been diagnosed with or were taking medication for any of the
following diseases: chronic heart, lung, or kidney disease; cerebro-vascular disease (including stroke);
cancer; hypertension; diabetes.
Obesity also increases the risk of developing severe COVID-19. In our analysis, an individual was
considered obese when his/her body mass index (BMI) was greater than or equal to 30 kg/m2.
In addition, self-reported health was included among the variables assessing baseline health needs,
and it was grouped into four categories (“excellent/very good”, “good”, “fair”, “poor”). Despite its
inherent subjectivity, this variable has been shown to be a good proxy for global health status, as well
as a reliable predictor of all-cause mortality (Idler and Benyamini, 1997) and the level of healthcare
use (DeSalvo et al., 2005).
Differences in the use of health services before the outbreak were captured through three additional
variables: the number of encounters with a general practitioner or specialist during the previous 12
months and whether they had visited a dentist at least once in the same time frame. The introduction
of healthcare utilisation at baseline into the models is particularly needed in the analysis of postponed
care because only care that was planned before the pandemic could have been postponed.
2.5. Empirical strategy
The empirical strategy involved a sequence of probit models that were successively estimated for the
three types of unmet needs during the first wave of the epidemic.
As a first step, the overall influence of economic vulnerability on outcomes was estimated, after
controlling for differences in unmet needs due to age, sex, relationship status (living alone or not), and
country of residence. This also enabled us to identify whether older individuals suffered more from
unmet needs than others, irrespective of their economic vulnerability status.
In the second step, we investigated how the overall effect of economic vulnerability evolved when
baseline differences in healthcare needs and utilisation were accounted for (full model). For all three
outcomes, the full model specification was re-estimated separately for each country to explore the
heterogeneity of the effects of economic vulnerability on unmet needs across Europe.
Finally, for each type of unmet need, the full model was re-estimated over the entire sample with the
addition of an interaction term between economic vulnerability and baseline self-reported health. This
interaction model reveals to what extent the social gradient of inequalities in access to healthcare
differed between originally healthy and unhealthy individuals.
53. Results
Table 1 provides a description of the sample’s average characteristics. Country-specific weights were
applied to ensure the country subsamples were representative of the national populations aged 50
years and over with regard to age, sex, and region (De Luca and Rossetti, 2018). The mean age of our
sample was 66.3 years, and 48% of the respondents were aged 65 years or older. Women represented
54% of the respondents.
3.1. Descriptive statistics
Unmet healthcare needs during the period are mainly revealed by the postponement of planned care
(25%) and, to a lesser extent, by situations in which people forwent care for fear of contracting COVID-
19 (12%) and were unable to obtain a medical appointment (5%) (Table 1). The proportion of
respondents who reported they experienced a postponement of at least one planned medical
encounter was notably high in several countries (Table 2), such as Luxembourg (54%), the Czech
Republic (37%), France (36%) and Belgium (35%). The variability between countries in forgoing care for
fear of contracting COVID-19 was lower and only weakly correlated with the intensity of the first wave
of the epidemic: the proportions of Germans (17%) and Swedes (15%) who have forgone care for fear
of COVID-19 were higher than the proportions observed in countries where the impact of the epidemic
has been greater, such as France (10%) and Spain (4%). The proportion of individuals who were unable
to obtain a medical appointment differed across countries as well, reaching 10% in France and 12% in
Latvia. These heterogeneous proportions across countries justify the adjustment of all models for
country fixed effects.
Table 1 provides additional insights. Eighty-three percent of individuals aged 50 years and over
reported suffering from a chronic condition or taking regular medical treatments for such a condition
in the interview conducted before the pandemic. This proportion was only 59% when the scope was
narrowed to only those chronic diseases predisposing patients to severe COVID-19. Regarding
healthcare utilisation, respondents reported an average of 4.1 contacts with general practitioners and
2.7 contacts with specialists during the 12 months before the interview. Additionally, an average of
57% of those people consulted a dentist at least once in the same period.
Thirty-six percent of the respondents admitted experiencing difficulties in making ends meet on their
household income and were therefore considered economically vulnerable. Older individuals were
significantly less economically vulnerable than younger seniors, whereas women were significantly
more economically vulnerable than men. Economically vulnerable people had, on average, worse
health than others. As a result, without adjustment for age or health status, they made more frequent
use of primary care, visiting their general practitioner an average of 4.9 times a year, compared to 3.6
times in the non-vulnerable population. This difference in healthcare utilisation was negligible for
specialist doctors and was reversed for dentists, which is reflectively of the unaffordability of dental
care for the lowest socio-economic groups.
Without adjustment for differences in other characteristics between the two sub-populations, people
who were economically vulnerable at baseline were less likely to have had at least one medical
treatment postponed during the first wave of the pandemic (22% versus 26%) and were as likely to
have been unable to obtain a medical appointment (6% versus 5%). No difference according to
economic vulnerability was observed in the probability of having forgone care for fear of contracting
COVID-19.
63.2. Overall impact of economic vulnerability on unmet needs
Table 3 shows the results of the two probit models estimated for each of the three indicators of unmet
needs.
After controlling for differences in age, sex, relationship status and country (step 1), relatively more of
the most economically vulnerable people reported having forgone medical care because of the fear of
COVID-19 (+1.9 percentage points) and not being able to get a medical appointment when needed
(+1.3 points). No significant difference between vulnerable and non-vulnerable individuals was
observed in the probability of experiencing a postponement of scheduled care. In addition, people
aged 65 to 85 years were significantly more likely than others to have had unmet needs during the
epidemic, especially with regard to forgoing care due to fear of COVID-19 and experiencing the
postponement of care, with an increasing risk with increasing age until the age of 79 years.
As expected, when introduced into the models (step 2), all the variables related to the need for
healthcare, except obesity, were positively correlated with each of the three types of unmet needs. A
self-reported global health status of “poor” rather than “excellent or very good” increased the
probability of having forgone care for fear of contracting COVID-19 by 3.4 percentage points, the risk
of postponement of care by 6.0 points, and the probability of having been unable to obtain a medical
appointment by 2.1 points.
It also appears that unmet needs have been even more pronounced among individuals suffering from
chronic conditions that are not specifically associated with an increased risk of severe forms of COVID-
19: patients suffering from non-specific chronic conditions were more likely than people with chronic
conditions that predisposed them to severe COVID-19 to have been refraining from healthcare (+4.2
points compared with +1.6 points), to have experienced the postponement of scheduled care (+8.0
versus +6.3) and to have been unable to obtain a medical appointment (+1.8 versus +0.7). The pre-
epidemic intensity of healthcare utilisation was also positively correlated with the likelihood of
reporting having faced barriers in accessing care during the pandemic. This result suggests that the
healthcare demand during the pandemic was correlated with initial healthcare habits, for a given
health status, which reflects preferences regarding health and healthcare and initial difficulties in
accessing health services.
Notably, the sign of the effect of age on unmet needs changed after controlling for the pre-epidemic
differences in care needs and utilisation between respondents: this suggests that healthcare needs
and utilisation are strongly and positively correlated with age. In the full model, the probability of
having had unmet needs during the first epidemic wave was much lower among the eldest individuals
than among those aged between 50 and 64 years for each indicator.
After controlling for baseline differences in health care needs and utilisation between individuals (step
2), the overall effects of economic vulnerability on the three measures of unmet needs slightly
decreased but did not significantly change compared to those obtained previously (step 1). This may
be explained by two reverse effects: economically vulnerable individuals may have higher pre-
epidemic needs than others, on average, but a lower utilisation of care for the same needs. In the end,
these two competing effects on unmet needs tend to cancel each other out. Based on the full model,
the magnitude of the effect of economic vulnerability was +1.0 points for forgoing care due to fear of
COVID-19, +0.9 points for the unavailability of medical appointments, and not significant for the
probability of experiencing the postponement of planned care.
73.3. Country-specific impact of economic vulnerability on unmet needs
The full specification model was then re-estimated on a country-by-country basis to determine the
heterogeneous influence of economic vulnerability on access to care across Europe (estimates
available upon request).
Although the effect of economic vulnerability on having forgone medical treatment for fear of infection
was significantly positive in the analysis including all countries, it varied substantially among countries
(Figure 1). The risk of forgoing care was higher among the most economically vulnerable individuals in
Israel (+10.9 percentage points), the Czech Republic (+8.3) and Sweden (+6.5). The same was not the
case everywhere: in Spain (-3.3 points), Finland (-3.6), Switzerland (-4.6) and Bulgaria (-6.2), the most
economically vulnerable people refrained from accessing medical care for fear of COVID-19 less
frequently than others.
A similar pattern was identified in the postponement of medical treatments during the first wave of
the epidemic (Figure 2). The non-significant impact of economic vulnerability on the probability of
having had care postponed in the sample including all countries masked the heterogeneous effects
across European countries: this particular risk affected economically vulnerable people more
frequently than others in Denmark (+9.4 points), France (+8.2) and Slovakia (+5.1), while it affected
them significantly less in a few other countries, particularly Italy (-6.8 points).
The positive effect of economic vulnerability on the unavailability of care was weaker than its effect
on forgoing care for fear of COVID-19 in the sample including all countries and was also more uniform
across countries (Figure 3). The most economically vulnerable faced more difficulties obtaining medical
appointments or treatments in Spain (+3.4 points) and Greece (+2.9), while they appeared to
experience fewer difficulties in Italy (-2.4).
The interpretation of these country-level differences is not straightforward. Social differences in
forgoing care for fear of COVID-19 may be more important in countries in which the most economically
vulnerable have been more likely to be exposed to the virus, and the variations in the risk of exposure
are themselves potentially correlated with the heterogeneous spread across and within countries
between the more and less privileged areas (Northern versus Southern Italy, Catalunya versus other
Spanish provinces, Paris versus other French regions). They could also be explained by country-level
differences in the type and strictness of social distancing policies and the social distribution of
individual preferences towards risk. Social differences in postponement and unavailability of care may
be more related to country-level variations in the magnitude of healthcare restrictions and the types
of care that have been cancelled (GPs versus specialists, public versus private sector, etc.) .
The international differences observed during the first wave of the epidemic do not seem to overlap
with the established evidence regarding the level of social inequalities in healthcare use and unmet
needs across European countries. For instance, the impact of economic vulnerability on care
postponement and the unavailability of care was significantly negative in Italy, although Italy is well
known for its high level of social inequality with regard to visits to GPs and specialists and unmet needs
due to cost, waiting time or distance (OECD, 2019). Conversely, highly positive effects were observed
in Spain, although the magnitudes of social inequalities in access to care in Spain and Italy are usually
alike. The health systems in both countries can also be considered similar in many aspects, and they
both had to deal with a particularly deadly first wave of the epidemic.
8In France and Slovakia, high levels of social inequalities in the postponement of care during the
pandemic were observed, which are consistent with those existing before the pandemic, namely, high
social inequalities in unmet needs because of cost in Slovakia and high social inequalities in specialist
care, which is more habitually planned, in France. In Greece, the greater effect of economic
vulnerability on the unavailability of care is consistent with the higher level of social inequality in access
to GPs before the pandemic. This pattern does not hold, however, for the other countries exhibiting
high social inequalities in the unavailability of care during the first wave of the epidemic, such as the
Netherlands and Spain.
3.4. Cumulative effects of economic vulnerability and baseline health status on unmet
needs
The final stage consisted of re-estimating the full model over the entire sample by adding an
interaction term between economic vulnerability and self-reported health status at baseline
(estimates available upon request).
For each of the three types of unmet needs considered, the effect of economic vulnerability was higher
among individuals reporting "poor" health before the epidemic than among those reporting "excellent
or very good" health (Figure 4). For example, among the former, the most economically vulnerable
were much more likely than others to have experienced the postponement of planned care (+3.5
points). Similarly, among individuals reporting a "poor" health before the epidemic, those in situations
of economic vulnerability were more likely to forgo care for fear of COVID-19 (+3.3), whereas the
reverse was true among those with "excellent or very good" health (-2.4). These results suggest that
social inequalities in access to care during the first epidemic wave were greater among the populations
with the greatest needs, who were potentially the oldest individuals. As a result, the pandemic may
increase social inequalities in health in the long term.
4. Discussion
The aim of this work was to investigate the social inequalities in access to healthcare during the first
wave of the COVID-19 epidemic among older European adults.
Postponing planned care (25%) and forgoing healthcare for fear of infection (12%) were greater
obstacles to accessing care for people over the age of 50 years than were difficulties in obtaining
medical treatments due to the unavailability of health providers (5%). Barriers to accessing care have
had greater effects on those who had the highest need and who used healthcare the most before the
outbreak, in particular the oldest individuals. After adjusting for age, sex, relationship status, country,
and care needs and utilisation before the pandemic, economically vulnerable individuals had an
additional 1.0% risk of forgoing care for fear of contracting COVID-19 and an additional 0.9% risk of
being unable to obtain a medical appointment when needed but did not have a significantly higher risk
of experiencing care postponement.
The increased risk of forgoing care for fear of COVID-19 among the most economically vulnerable may
be rooted in initial differences in health literacy – i.e., the capacity to obtain, communicate, process,
and understand basic health information and services to make appropriate health decisions –
(Sørensen et al., 2015) or the degree of risk aversion between wealthier and poorer individuals (Barsky
et al., 1997).
9Several factors may explain the higher probability of having tried unsuccessfully to obtain a medical
appointment among the most economically vulnerable, even after controlling for their pre-pandemic
care needs and utilisation. The poorest individuals may have been more discouraged by the difficulties
caused by care rationing and the complexity of the steps that needed to be taken to obtain an
appointment. Restrictions and shortages may have been more substantial for certain types of care that
are more often used by the poorest population, such as hospital care or care delivered by public sector
doctors. On the other hand, the deferral of previously scheduled care seems to have occurred more
equally, with no observable difference according to the socio-economic status of the patients.
Social inequalities in access to care during the first wave of the epidemic emerged more distinctly
among people initially in poor health for all three types of unmet needs. Among those in poor health
at baseline, the economically vulnerable had a 3.5-point greater likelihood of experiencing the
postponement of scheduled care, a 3.4-point greater likelihood of forgoing care for fear of COVID-19,
and a 2.0-point greater likelihood of being unable to obtain a medical appointment. Thus, the
reorganisation of health systems caused by the pandemic did not result in a prioritisation of care based
exclusively on need and health status criteria, as social inequalities in access to care over the period
could be observed in the sickest population.
The impact of economic vulnerability on unmet needs differed substantially among countries, with a
notably low impact of economic vulnerability on the postponement of care and the unavailability of
new appointments in Italy. However, the magnitude of social differences in unmet needs does not
appear to have been exacerbated during the pandemic in the countries with the highest initial levels
of social inequality in access to care or unmet needs. This finding would need to be examined in future
research to shed light on the differences in social inequalities in access to care during the pandemic
according to the organisation of health systems and the various strategies used to confront the
pandemic in each country.
Besides the possible selection bias inherent in data obtained with a telephone survey such as SHARE
Corona Survey, the questions used in this survey to measure unmet needs during the pandemic had
never been tested before: it is unclear exactly how respondents understood these. The measurement
of health also relied on the list of chronic diseases provided by SHARE, which, however broad, does
not cover all chronic or acute conditions. Likewise, the use of a single subjective question on
“difficulties making ends meet” to characterise economic vulnerability, although parsimonious and, in
principle, entirely comparable across countries, could be questioned.
Despite these limitations, this work highlights the risk that these periods of care rationing may
ultimately lead to an exacerbation of social inequalities in healthcare and in health. Data from the
second wave of the SHARE Corona Survey will be valuable for studying the potential effects of unmet
care needs on social inequalities in health in the long term.
10References
Allin S., Grignon M., & Le Grand J. (2010) Subjective unmet need and utilization of healthcare services in
Canada: what are the equity implications? Social Science & Medicine, 70(3), 465-472.
DOI: 10.1016/j.socscimed.2009.10.027.
Barsky, R. B., Juster, F. T., Kimball, M. S., & Shapiro, M. D. (1997). Preference parameters and behavioural
heterogeneity: An experimental approach in the health and retirement study. The Quarterly Journal of
Economics, 112(2), 537-579.
Bertier, M., Luyten, J., & Tubeuf, S. (2020), Renoncement aux soins médicaux durant le confinement au printemps
2020 en Belgique : effet de l’offre ou de la demande ? Université Catholique de Louvain. Mimeo.
Börsch-Supan, A., M. Brandt, C. Hunkler, T. Kneip, J. Korbmacher, F. Malter, B. Schaan, S. Stuck, S. Zuber (2013).
Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). International Journal of
Epidemiology. DOI: 10.1093/ije/dyt088
Börsch-Supan, A. (2020). Survey of Health Ageing and Retirement in Europe (SHARE) Wave 8. Release version:
0. SHARE-ERIC. Preliminary data set.
Chatterji P., & Li Y. (2020). Effects of the Covid-19 pandemic on outpatient providers in the US. National Bureau
of Economic Research (NBER), Working Paper Series, No. 27173.
CNAMTS (2020). L’actualité en quelques chiffres : les remboursements de soins du régime général à fin avril
2020.
Czeisler M.E., Marynak, K., Clarke, K.E.N, Salah, Z., Shakya, I., Thierry, J.M, et al. (2020). Delay or Avoidance of
Medical Care Because of COVID-19–Related Concerns. US Department of Health and Human Services / Centers
for Disease Control and Prevention, MMWR (Morbidity and Mortality Weekly Report), 69(36).
De Luca, G. and Rossetti, C. (2018). Stata program to compute calibrated weights from scientific use file and
additional database. SERISS report D2.10.
DeSalvo, K. B., Fan, V. S., McDonell, M. B., & Fihn, S. D. (2005). Predicting mortality and healthcare utilization
with a single question. Health services research, 40(4), 1234-1246.
Dourgnon P., Jusot F., Fantin R. (2012), Payer nuit gravement à la santé : une étude de l’impact du
renoncement financier aux soins sur l’état de santé, Economie Publique, 28-29, 123-147.
Fahey, T. (2007) The case for an EU-wide measure of poverty. European Sociological Review, 23 (1): 35-47
Goedhart, T., Halberstadt, V., Kapteyn, A., & Van Praag, B. (1977). The poverty line: concept and measurement.
Journal of Human Resources, 503-520.
Gonzalez, D., Zuckerman, S., Kenney, G.M, & Karpman, M. (2020). Almost Half of Adults in Families Losing Work
during the Pandemic Avoided Healthcare Because of Costsor COVID-19 Concerns. Urban Institute, Policy brief,
July 2020.
Hanna, T. P., Evans, G. A., & Booth, C. M. (2020). Cancer, COVID-19 and the precautionary principle: prioritizing
treatment during a global pandemic. Nature Reviews Clinical Oncology, 17(5), 268-270.
Hartnett, K.P., Kite-Powell, A., DeVies, J., Coletta, M.A., Boehmer, T.K., Adjemian, J., Gundlapalli, A.V. (2020).
Impact of the COVID-19 Pandemic on Emergency Department Visits — United States, January 1, 2019 – May 30,
2020. Centers for Disease Control and Prevention (CDC), Morbidity and Mortality Weekly Report (MMWR),
69(23), 699–704
Idler, E. L., & Benyamini, Y. (1997). Self-rated health and mortality: a review of twenty-seven community
studies. Journal of health and social behavior, 21-37.
Jürges H., & Stella L. (2019). The social dynamics of unmet need, catastrophic healthcare expenses and
satisfaction with health insurance coverage in Börsch-Supan, A., J. Bristle, K. Andersen-Ranberg, A. Brugiavini, F.
Jusot, H. Litwin, G. Weber (Eds.) (2019). Health and Socio-Economic Status over the Life Course. First Results
from SHARE Waves 6 and 7. Berlin: De Gruyter.
Ko H., (2016). Unmet healthcare needs and health status: Panel evidence from Korea. Health policy, 120(6),
646-653.
11Kranz, A.M., Gahlon, G., Dick, A.W., & Stein, B.D. (2021). Characteristics of US Adults Delaying Dental Care Due
to the COVID-19 Pandemic. JDR Clinical & Translational Research, 6(1), 8-14. DOI: 10.1177/2380084420962778.
Litwin, H., & Sapir, E. V. (2009). Forgone healthcare due to cost among older adults in European countries and
in Israel. European journal of ageing; 6(3), 167-176.
Maringe, C., Spicer, J., Morris, M., Purushotham, A., Nolte, E., Sullivan, R., Aggarwal, A. (2020). The impact of
the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-
based, modelling study. The Lancet oncology, 21(8), 1023-1034.
Marmot, M., Friel, S., Bell, R., Houweling, T., Taylor, S., & the Commission on Social Determinants of Health
(2008). Closing the gap in a generation: health equity through action on the social determinants of health. The
Lancet, 372, 1661-1669.
Marmot M. and Bell R. (2016). Social inequalities in health: a proper concern of epidemiology. Ann Epidemiol.,
26(4), 238-40.
Mehrotra, A., Chernew, M., Linetsky, D., Hatch, H. & Cutler, D. (2020). The Impact of the COVID-19 Pandemic
on Outpatient Visits: A Rebound Emerges, Commonwealth Fund, To the Point (blog).
DOI: https://doi.org/10.26099/ds9e-jm36
Negopdiev, D., Collaborative, C., & Hoste, E. (2020). Elective surgery cancellations due to the COVID-19
pandemic: global predictive modelling to inform surgical recovery plans. British Journal of Surgery, 107(11),
1440-1449.
NHS (2020). A&E Attendances and Emergency Admissions 2020-21. NHS data collection.
https://www.england.nhs.uk/statistics/statistical-work-areas/ae-waiting-times-and-activity/ae-attendances-
and-emergency-admissions-2020-21/
Organisation for Economic Cooperation and Development (OECD) (2019). Health for Everyone? Social
Inequalities in Health and Health Systems. OECD Health Policy Studies.
World Health Organisation (WHO) (2020). Pulse survey on continuity of essential health services during the
COVID-19 pandemic: interim report, 27 August 2020. World Health Organization.
https://apps.who.int/iris/handle/10665/334048.
Pikoulis, E., Solomos, Z., Riza, E., Puthoopparambil, S. J., Pikoulis, A., Karamagioli, E., & Puchner, K. P. (2020).
Gathering evidence on the decreased emergency room visits during the coronavirus disease 19
pandemic. Public Health, 185, 42.
Rosenbaum, L. (2020). Facing Covid-19 in Italy—ethics, logistics, and therapeutics on the epidemic’s front
line. New England Journal of Medicine, 382(20), 1873-1875.
Scherpenzeel, A., Axt, K., Bergmann, M., Douhou, S., Oepen, A., Sand, G., Schuller, K., Stuck, S., Wagner, M., &
Börsch-Supan, A. (2020). Collecting survey data among the 50+ population during the COVID-19 outbreak: The
Survey of Health, Ageing and Retirement in Europe (SHARE). Survey Research Methods, 14(2), 217-221.
Søreide, K., Hallet, J., Matthews, J. B., Schnitzbauer, A. A., Line, P. D., Lai, P. B. S., ... & Teh, C. S. (2020).
Immediate and long‐term impact of the COVID‐19 pandemic on delivery of surgical services. The British journal
of surgery.
Sørensen, K., Pelikan, J.M, Röthlin, F., Ganahl, K., Slonska, Z., Doyle, G., et al. on behalf of the HLS-EU
Consortium, Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU).
European Journal of Public Health, 25(6), 1053-58, DOI: https://doi.org/10.1093/eurpub/ckv043
Williamson, E. J., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., ... & Cockburn, J. (2020).
Factors associated with COVID-19-related death using OpenSAFELY. Nature, 584(7821), 430-436.
Zheng Z., Peng F., Xu B., Zhao J., Liu H., …, Tang W., 2020, Risk factors of critical & mortal COVID-19 cases: A
systematic literature review and meta-analysis. Journal of Infection, 81(2): 16-25.
Zhou F., Yu T., Du R., Fan G., …, Cao B., 2020, Clinical course and risk factors for mortality of adult inpatients
with COVID-19 in Wuhan, China: a retrospective cohort study, The Lancet, 395(10229): 1054-1062.
Ziedan, E., Simon, KI. & Wing, C. (2020) Effects of State COVID-19 Closure Policy on NON-COVID-19 Healthcare
Utilization. National Bureau of Economic Research (NBER), Working Paper Series, No. 27621.
12Tables & Figures
Table 1 – Sample description, baseline healthcare needs and utilisation, and unmet needs during
the first wave of the epidemic stratified by economic vulnerability
Not economically Economically
vulnerable vulnerable Statistical
All (individuals (individuals significance of the
respondents WITHOUT WITH difficulties difference
difficulties in in making ends between
making ends meet) meet) economically
vulnerable and not
N 31,928 19,447 12,481
vulnerable
Weighted N (in million) 171.5 109.8 61.7
Unmet healthcare needs during the first epidemic
wave
Forgoing medical care because of the fear of
12 12 12 NS
contracting COVID-19 (%)
Planned care postponed by the healthcare provider (%) 25 26 22 ***
Impossibility of obtaining medical
5 5 6 **
appointment/treatment (%)
Main characteristics before outbreak
Age (mean) 66.3 66.6 65.8 ***
Aged 65 years or older (%) 48 50 45 ***
Women (%) 54 52 56 **
In a relationship (%) 64 67 60 ***
Employed, among those aged 50-64 years (%) 62 71 47 ***
Economic vulnerability (%) 36
Health status and healthcare utilisation before
outbreak
Diagnosed with a chronic condition or regular
83 82 85 *
medication (%)
Chronic condition linked with severe COVID-19 (%) 59 56 65 ***
Obesity: body mass index (BMI) ≥ 30 (%) 23 20 26 ***
Self-assessed health: fair or poor (%) 37 30 49 ***
Number of contacts with a GP in the last 12 months (%) 4.1 3.6 4.9 ***
Number of contacts with a specialist in the last 12
2.7 2.6 2.8 NS
months (%)
Visited a dentist in the last 12 months (%) 57 68 37 ***
Data: Preliminary SHARE wave 8 release 0. Conclusions are preliminary. Sample: N = 31,928 respondents in 26 countries.
Notes: weighted frequencies; Student’s t-tests were performed to test the equality of means for continuous variables, and Pearson's
chi2 tests were performed for binary variables; * p-value < 0.1 ** p-value < 0.05 *** p-value < 0.01, NS: no significant difference
14Table 2 – Unmet healthcare needs during the first wave of the epidemic by country
Forgoing medical care Impossibility of obtaining
Planned care
because of the fear of medical appointment /
postponed (%)
COVID-19 (%) treatment (%)
All countries 12 25 5
DE 17 19 3
SE 16 18 4
NL 5 28 3
ES 4 25 4
IT 17 26 7
FR 10 36 10
DK 10 31 4
GR 16 11 5
CH 14 28 3
BE 12 35 9
IL 27 24 9
CZ 19 37 3
PL 9 28 6
LU 23 54 7
HU 6 22 4
SL 4 33 3
EE 10 24 8
HR 9 23 3
LT 14 28 12
BG 10 1 1
CY 11 15 4
FI 8 19 5
LV 13 14 5
MT 11 36 3
RO 6 8 6
SK 15 20 5
Data: Preliminary SHARE wave 8 release 0. Conclusions are preliminary. Sample: N = 31,928 respondents in 26 countries.
Notes: weighted frequencies.
15Table 3 – Effects on the probability of unmet healthcare needs during the first wave of the epidemic
Forgoing medical Impossibility of obtaining
Planned care
care because of the medical appointment /
postponed
fear of COVID-19 treatment
Step 1 Step 2 Step 1 Step 2 Step 1 Step 2
Age: 65-69 (Ref. 50-64) 0.011** 0.003 0.012* -0.005 0.001 -0.002
Age: 70-74 (Ref. 50-64) 0.017*** 0.003 0.016** -0.014* -0.002 -0.008**
Age: 75-79 (Ref. 50-64) 0.016*** -0.003 0.025*** -0.014* -0.002 -0.010**
Age: 80-84 (Ref. 50-64) 0.023*** 0.001 -0.008 -0.046*** -0.002 -0.010**
Age: 85+ (Ref. 50-64) -0.008 -0.025*** -0.057*** -0.080*** -0.014*** -0.020***
Women 0.046*** 0.041*** 0.029*** 0.020*** 0.005** 0.003
In a relationship 0.003 0.002 0.004 -0.002 0.001 0.001
Economic vulnerability 0.019*** 0.010** 0.006 -0.007 0.013*** 0.009***
1+ chronic condition linked with severe
0.010** 0.041*** 0.002
COVID-19
1+ other chronic condition 0.031*** 0.045*** 0.012***
BMI ≥ 30 0.004 0.002 0.001
Self-assessed health: Excellent or very good
-0.018*** -0.011* -0.006*
(Ref. good)
Self-assessed health: fair (Ref. good) 0.021*** 0.027*** 0.007**
Self-assessed health: poor (Ref. good) 0.026*** 0.028*** 0.014***
Contacts with a GP: 1 or 2 (Ref. 0) 0.006 0.051*** 0.008**
Contacts with a GP: 3 to 5 (Ref. 0) 0.013** 0.048*** 0.008*
Contacts with a GP: 6+ (Ref. 0) 0.011 0.059*** 0.013***
Contacts with a specialist: 1 or 2 (Ref. 0) 0.035*** 0.085*** 0.013***
Contacts with a specialist: 3 to -5 (Ref. 0) 0.048*** 0.146*** 0.022***
Contacts with a specialist: 6+ (Ref. 0) 0.054*** 0.187*** 0.035***
Have seen a dentist 0.023*** 0.065*** 0.012***
Country fixed effects Yes Yes Yes Yes Yes Yes
Data: Preliminary SHARE wave 8 release 0. Conclusions are preliminary. Sample: N = 31,928 respondents in 26 countries.
Notes: (unweighted) models including all countries; average marginal effects displayed; * p-value < 0.1 ** p-value < 0.05 *** p-value < 0.01
16Figure 1 – Probability of having forgone medical treatment for fear of COVID-19:
effect of economic vulnerability by country (full model)
How to read: The results are expressed as average marginal effects, which can be understood as percentage points of
increase/decrease in the probability of the outcome.
Data: Preliminary SHARE wave 8 release 0. Conclusions are preliminary. Sample: N = 31,928 respondents in 26 countries.
Notes: Full models were estimated country by country; BG was excluded (too few individuals had a medical appointment postponed);
the error bars represent 95% confidence intervals.
Figure 2 – Probability of having had planned medical care postponed:
effect of economic vulnerability by country (full model)
How to read: The results are expressed as average marginal effects, which can be understood as percentage points of
increase/decrease in the probability of the outcome.
Data: Preliminary SHARE wave 8 release 0. Conclusions are preliminary. Sample: N = 31,928 respondents in 26 countries.
Notes: Full models were estimated country by country; BG was excluded (too few individuals had a medical appointment postponed);
the error bars represent 95% confidence intervals.
17Figure 3 – Probability of having been unable to obtain a medical appointment/treatment:
effect of economic vulnerability, by country (full model)
How to read: The results are expressed as average marginal effects, which can be understood as percentage points of
increase/decrease in the probability of the outcome.
Data: Preliminary SHARE wave 8 release 0. Conclusions are preliminary. Sample: N = 31,928 respondents in 26 countries.
Notes: Full models were estimated country by country; BG was excluded (too few individuals had a medical appointment
postponed); the error bars represent 95% confidence intervals.
Figure 4 –Effects of economic vulnerability on unmet healthcare needs
according to baseline self-assessed health (all countries)
How to read: The results are expressed as average marginal effects, which can be understood as percentage points of
increase/decrease in the probability of the outcome.
Data: Preliminary SHARE wave 8 release 0. Conclusions are preliminary. Sample: N = 31,928 respondents in 26 countries.
Notes: Full models including all countries and adjusted for differences in unmet needs due to age, sex, relationship status (living
alone or not), country of residence and for baseline differences in health care needs and utilisation; the error bars represent 95%
confidence intervals.
18You can also read
